Deep Eyes solar Panels

Can deep learning be used for solar PV image analysis?

In this paper, we presented a first deep learning based application for solar PV image analysis. We proposed a novel fully convolutional neural network based approach which takes an RGB image of solar panel and environmental factors (optional) as inputs to predict power loss, soiling localization, and soiling category.

What is deepsolareye?

DeepSolarEye: Power Loss Prediction and Weakly Supervised Soiling Localization via Fully Convolutional Networks for Solar Panels. Accepted at IEEE Winter Conference on Applications of Computer Vision (WACV), 2018. pdf Dataset Coming Soon! No description, website, or topics provided.

How to train a deep solar eye model?

A simplified overview of DeepSolarEye is presented in Figure 2. Ideally, to train such a model, we need two kinds of labels: 1) power loss, and 2) localization mask. In our approach, DeepSolarEye, we bypass the explicitly labeled localization data requirement by using power loss label as weak supervision for localization.

Does deepsolareye predict power loss and soiling area simultaneously?

In this paper, we present a novel end-to-end fully con-volutional neural network, DeepSolarEye, that simultaneously predicts the power loss and localizes soiling area from the RGB Fig. 2: Overview of our method, DeepSolarEye, that predicts impact on the power loss and the soiling area simultaneously. image of solar panel.